2 Data & Basic Analysis

2.1 Data Verification

It’s always a good idea to make sure the data are input and interpreted properly. Mea culpa. I forgot to do this. So, this step was actually slipped in later in the analysis stream but is presented here as this is a more logical location.

Show the code

# Load necessary libraries
if (!require("gt")) install.packages("gt")
if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyr")) install.packages("tidyr")
if (!require("glue")) install.packages("glue")

library(gt)
library(dplyr)
library(tidyr)
library(glue)

# 1. Define Helper Function for Text Color
get_text_color <- function(hex) {
  if (is.na(hex)) return("black") 
  rgb <- col2rgb(hex)
  lum <- (0.299 * rgb[1] + 0.587 * rgb[2] + 0.114 * rgb[3]) / 255
  if (lum > 0.5) return("black") else return("white")
}

# 2. Define the Cell Formatting Function (Scalar Version)
fmt_color_cell_worker <- function(name, hex) {
  # Handle NAs (Blank Cells)
  if (is.na(name) || is.na(hex)) {
    return("<div style='background-color: white; width: 100%; height: 100%;'></div>")
  }
  
  # Determine text color based on background
  text_col <- get_text_color(hex)
  
  # Return HTML
  glue(
    "<div style='
      background-color: {hex}; 
      color: {text_col}; 
      width: 100%; 
      height: 100%; 
      padding: 8px; 
      border-radius: 4px;
      font-weight: bold;
      text-align: center;
      font-size: 12px;'>
      {name}
    </div>"
  )
}

# 3. Vectorize the Function
fmt_color_cell <- Vectorize(fmt_color_cell_worker)

# 4. Create the Data (Historically Accurate)
data_raw <- tribble(
  ~Year, ~Pantone_Name, ~Pantone_Hex, ~BM_Name, ~BM_Hex, ~SW_Name, ~SW_Hex, ~Akzo_Name, ~Akzo_Hex, ~Glid_Name, ~Glid_Hex, ~Dunn_Name, ~Dunn_Hex, ~Behr_Name, ~Behr_Hex, ~Val_Name, ~Val_Hex,
  
  2026, "Cloud Dancer", "#F0EEE9", "Silhouette", "#4A413C", "Univ. Khaki", "#BCA68E", "Slow Swing", "#2C3E50", "Warm Mahogany", "#9D4A3C", "Midnight Garden", "#2E4035", "Hidden Gem", "#6A867F", "Warm Eucalyp.", "#7A8B78",
  2025, "Mocha Mousse", "#A47864", "Cinn. Slate", "#AA8C96", "Quietude", "#A3AF9D", "True Joy", "#F7C04A", "Purple Basil", "#6A4B5F", "Caramelized", "#B58463", "Rumors", "#8A3324", "Encore", "#2C5282",
  2024, "Peach Fuzz", "#FFBE98", "Blue Nova", "#5B6C91", "Upward", "#B4BEC3", "Sweet Embrace", "#E0C7D5", "Limitless", "#EFDECD", "Skipping Stones", "#7DA6B3", "Cracked Pepper", "#4E5052", "Renew Blue", "#97C5C9",
  2023, "Viva Magenta", "#BE3455", "Rasp. Blush", "#D25A46", "Redend Point", "#AE8E7E", "Wild Wonder", "#D2C29D", "Vining Ivy", "#3A6A6C", "Terra Rosa", "#A66E6A", "Blank Canvas", "#F0ECE2", NA, NA,
  2022, "Very Peri", "#6667AB", "October Mist", "#A3AA9E", "Evergreen Fog", "#95978A", "Bright Skies", "#87A9C3", "Guacamole", "#8C9C74", "Art & Craft", "#947B64", "Breezeway", "#B8CBC0", NA, NA,
  2021, "Ult. Gray", "#939597", "Aegean Teal", "#647882", "Urbane Bronze", "#545E60", "Brave Ground", "#8D7F71", "Transcend", "#D0C5B3", "Wild Blue", "#7D8F9E", "Canyon Dusk", "#C09277", NA, NA,
  2020, "Classic Blue", "#0F4C81", "First Light", "#EBE1E1", "Naval", "#2F3D4C", "Tranquil Dawn", "#C2CDBE", "Chin. Porcelain", "#304B64", "Minty Fresh", "#B6E0D2", "Back to Nature", "#97A878", NA, NA,
  2019, "Living Coral", "#FF6F61", "Metropolitan", "#AFB4B4", "Cavern Clay", "#D1866A", "Spiced Honey", "#C49A6C", "Night Watch", "#004746", "Spice of Life", "#8D4E3C", "Blueprint", "#4D6C8C", NA, NA,
  2018, "Ultra Violet", "#5F4B8B", "Caliente", "#AF2D2D", "Oceanside", "#195564", "Heart Wood", "#B5A29D", "Black Flame", "#202226", "Green Hour", "#3D4F46", "In The Moment", "#7E9995", NA, NA,
  2017, "Greenery", "#88B04B", "Shadow", "#5F505F", "Poised Taupe", "#8C827D", "Denim Drift", "#7E8E9F", "Violet Verbena", "#7A5C6D", "Honey Glow", "#E3B668", "Comfortable", "#D5CDB5", NA, NA,
  2016, "Rose Quartz", "#F7CAC9", "Simply White", "#F3F4ED", "Alabaster", "#EDEAE0", "Ochre Gold", "#D09D52", "Paradise Found", "#7B9683", NA, NA, NA, NA, NA, NA
)

# 5. Process the DataFrame
df_formatted <- data_raw %>%
  mutate(
    Pantone = fmt_color_cell(Pantone_Name, Pantone_Hex),
    Benjamin_Moore = fmt_color_cell(BM_Name, BM_Hex),
    Sherwin_Williams = fmt_color_cell(SW_Name, SW_Hex),
    AkzoNobel = fmt_color_cell(Akzo_Name, Akzo_Hex),
    Glidden = fmt_color_cell(Glid_Name, Glid_Hex),
    Dunn_Edwards = fmt_color_cell(Dunn_Name, Dunn_Hex),
    Behr = fmt_color_cell(Behr_Name, Behr_Hex),
    Valspar = fmt_color_cell(Val_Name, Val_Hex)
  ) %>%
  select(Year, Pantone, Benjamin_Moore, Sherwin_Williams, AkzoNobel, Glidden, Dunn_Edwards, Behr, Valspar)

# 6. Handle Split Years (The Fix)
# Instead of complex HTML, we use standard cells with "multi-color" and white background
df_formatted$Pantone[df_formatted$Year == 2021] <- fmt_color_cell("multi-color", "#FFFFFF")
df_formatted$Pantone[df_formatted$Year == 2016] <- fmt_color_cell("multi-color", "#FFFFFF")

# 7. Generate Table
gt_table <- df_formatted %>%
  gt() %>%
  fmt_markdown(columns = everything()) %>%
  cols_label(
    Year = "Year",
    Pantone = "Pantone",
    Benjamin_Moore = "Benj. Moore",
    Sherwin_Williams = "Sherwin-W.",
    AkzoNobel = "AkzoNobel",
    Glidden = "Glidden",
    Dunn_Edwards = "Dunn-Edw.",
    Behr = "Behr",
    Valspar = "Valspar"
  ) %>%
  tab_header(
    title = md("**The Spectrum of Choice (2016–2026)**"),
    subtitle = "Color of the Year selections (Blank cells indicate no single COTY selected)"
  ) %>%
  cols_width(
    Year ~ px(50),
    everything() ~ px(100)
  ) %>%
  tab_style(
    style = list(cell_fill(color = "#f4f4f4"), cell_text(weight = "bold")),
    locations = cells_body(columns = Year)
  ) %>%
  tab_style(
    style = list(cell_text(weight = "bold", size = "large")),
    locations = cells_column_labels()
  )

# Print
gt_table

Year	Pantone	Benj. Moore	Sherwin-W.	AkzoNobel	Glidden	Dunn-Edw.	Behr	Valspar
The Spectrum of Choice (2016–2026)
Color of the Year selections (Blank cells indicate no single COTY selected)
2026	Cloud Dancer Cloud Dancer	Silhouette Silhouette	Univ. Khaki Univ. Khaki	Slow Swing Slow Swing	Warm Mahogany Warm Mahogany	Midnight Garden Midnight Garden	Hidden Gem Hidden Gem	Warm Eucalyp. Warm Eucalyp.
2025	Mocha Mousse Mocha Mousse	Cinn. Slate Cinn. Slate	Quietude Quietude	True Joy True Joy	Purple Basil Purple Basil	Caramelized Caramelized	Rumors Rumors	Encore Encore
2024	Peach Fuzz Peach Fuzz	Blue Nova Blue Nova	Upward Upward	Sweet Embrace Sweet Embrace	Limitless Limitless	Skipping Stones Skipping Stones	Cracked Pepper Cracked Pepper	Renew Blue Renew Blue
2023	Viva Magenta Viva Magenta	Rasp. Blush Rasp. Blush	Redend Point Redend Point	Wild Wonder Wild Wonder	Vining Ivy Vining Ivy	Terra Rosa Terra Rosa	Blank Canvas Blank Canvas
2022	Very Peri Very Peri	October Mist October Mist	Evergreen Fog Evergreen Fog	Bright Skies Bright Skies	Guacamole Guacamole	Art & Craft Art & Craft	Breezeway Breezeway
2021	multi-color multi-color	Aegean Teal Aegean Teal	Urbane Bronze Urbane Bronze	Brave Ground Brave Ground	Transcend Transcend	Wild Blue Wild Blue	Canyon Dusk Canyon Dusk
2020	Classic Blue Classic Blue	First Light First Light	Naval Naval	Tranquil Dawn Tranquil Dawn	Chin. Porcelain Chin. Porcelain	Minty Fresh Minty Fresh	Back to Nature Back to Nature
2019	Living Coral Living Coral	Metropolitan Metropolitan	Cavern Clay Cavern Clay	Spiced Honey Spiced Honey	Night Watch Night Watch	Spice of Life Spice of Life	Blueprint Blueprint
2018	Ultra Violet Ultra Violet	Caliente Caliente	Oceanside Oceanside	Heart Wood Heart Wood	Black Flame Black Flame	Green Hour Green Hour	In The Moment In The Moment
2017	Greenery Greenery	Shadow Shadow	Poised Taupe Poised Taupe	Denim Drift Denim Drift	Violet Verbena Violet Verbena	Honey Glow Honey Glow	Comfortable Comfortable
2016	multi-color multi-color	Simply White Simply White	Alabaster Alabaster	Ochre Gold Ochre Gold	Paradise Found Paradise Found

As explained earlier, the margins of this image were removed so that the names of the companies weren’t in the image. The AI engine was pretty good at guessing the companies. I helped to make sure that were all correct.

2.2 General Analysis

The AI engine responded to the question that I phrased earlier regarding generalizations about colors and whether there are any trends in the choices. Here is the response.

Based on the chart provided and current design trends for 2026, we can identify several distinct generalizations and shifts in color philosophy over the last decade.

The columns in your chart correspond to major color authorities. By cross-referencing the specific colors (like the 2016 and 2021 “split” swatches and the 2026 selections), we can identify the key players:

Column 1: Pantone (Famous for the split “Rose Quartz/Serenity” in 2016 and “Ultimate Gray/Illuminating” in 2021).
Column 2: Benjamin Moore (Note the shift to “Silhouette” dark charcoal/brown in 2026).
Column 3: Sherwin-Williams (Note the “Universal Khaki” in 2026).
Columns 4–8: Other major paint brands: AkzoNobel, Glidden, Dunn Edwards, Behr, and Valspar. (This is edited to reflect the information that I gave the AI engine)

Here are the generalizations and trends visible in the table:

1. The “Mood” Timeline

The colors serve as a timeline of the global emotional state (the zeitgeist):

2015–2019 (The Exploration Era): The colors were experimental and varied. We see bold purples (Ultra Violet, 2018), vibrant corals (Living Coral, 2019), and spicy reds.
2020–2021 (The Crisis & Safety Era): As the pandemic hit, the colors shifted dramatically to “safety” colors. Blue (classic, calm, stable) dominates 2020. 2021 introduces Grey (resilience) and Yellow (hope).
2022–2024 (The Healing & Nesting Era): Post-pandemic, the trend moved to “biophilic” colors—tones that mimic nature to make homes feel restorative. This is the era of sage greens (2022) and warm, fuzzy peaches/terracottas (2023–2024).
2025–2026 (The “New Foundations” Era): The most recent rows show a distinct shift away from pastels toward Deep Moody Tones (charcoal, dark chocolate) and Blank Slates (White).

2. 2026 is the Year of the “Reset”

The top row (2026) shows a fascinating divergence between “High Fashion” and “Home Decor”:

The “Clean Slate”: Pantone (Column 1) selected Cloud Dancer (White). After years of bold colors, this signals a desire to wipe the slate clean and start fresh—a “silence” amidst the noise.
The “Grounding”: In contrast, the paint brands (Cols 2 & 3) chose Deep Browns and Khakis (e.g., Benjamin Moore’s Silhouette and Sherwin-Williams’ Universal Khaki). This suggests that for interiors, people want stability and earthiness rather than stark minimalism.

3. Green is the “New Neutral”

If you look at the right half of the chart (Columns 6, 7, 8), you will see a massive amount of Green appearing consistently, especially in 2022 and 2026.

While Grey used to be the default neutral (see 2016–2019), Green has replaced it.
In 2026 specifically, while the big names went White/Brown, the consumer brands (Behr, Valspar, Glidden) all selected variations of Jade, Eucalyptus, and Olive. This generalization holds true: When in doubt, the paint industry bets on Green.

4. The “Split” Anomaly

The chart highlights two unique instances in Column 1 (Pantone) where two colors were chosen instead of one:

2016 (Rose Quartz & Serenity): A soft pink and blue. This was a commentary on gender fluidity and mindfulness.
2021 (Illuminating & Ultimate Gray): A bright yellow and solid grey. This represented “light at the end of the tunnel” (Yellow) supported by fortitude (Grey) during the pandemic recovery.

Summary Table of Eras

Era	Dominant Vibe	Key Colors
Pre-2020	Bold, Expressive	Ultra Violet, Living Coral, Greenery
2020-2021	Safety, Resilience	Classic Blue, Ultimate Gray, Naval
2022-2024	Comfort, Nature	Peach Fuzz, Sage Green, Terra Cotta
2025-2026	Grounding, Reset	White, Dark Brown, Khaki, Deep Jade